Identifying Individuals in a Complex Mixture of DNA with Unknown Ancestry
Sampson Joshua and
Zhao Hongyu
Additional contact information
Sampson Joshua: Yale University
Zhao Hongyu: Yale University
Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 29
Abstract:
A new test was recently developed that could use a high-density set of single nucleotide polymorphisms (SNPs) to determine whether a specific individual contributed to a mixture of DNA. The test statistic compared the genotype for the individual to the allele frequencies in the mixture and to the allele frequencies in a reference group. This test requires the ancestries of the reference group to be nearly identical to those of the contributors to the mixture. Here, we first quantify the bias, the increase in type I and type II error, when the ancestries are not well matched. Then, we show that the test can also be biased if the number of subjects in the two groups differ or if the platforms used to measure SNP intensities differ. We then introduce a new test statistic and a test that only requires the ancestries of the reference group to be similar to the individual of interest, and show that this test is not only robust to the number of subjects and platform, but also has increased power of detection. The two tests are compared on both HapMap and simulated data.
Keywords: DNA mixture; genomics; forensics; SNP; identify (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.2202/1544-6115.1469 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:37
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html
DOI: 10.2202/1544-6115.1469
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().